In this paper, an adaptive approach to the enhancement of speech signals is developed based on auditory spectral change. The algorithm is motivated by sensitivity of aural biologic systems to signal dynamics, by evidence that noise is aurally masked by rapid changes in a signal, and by analogies to these two aural phenomena in biologic visual processing. Emphasis is on preserving nonstationarity, i.e., speech transient and time-varying components, such as plosive bursts, formant transitions, and vowel onsets, while suppressing additive noise. The essence of the enhancement technique is a Wiener filter that uses a desired signal spectrum whose estimation adapts to stationarity of the measured signal. The degree of stationarity is derived from a signal change measurement, based on an auditory spectrum that accentuates change in spectral bands. The adaptive filter is applied in an unconventional overlap-add analysis/synthesis framework, using a very short 4-ms analysis window and a 1-ms frame interval. In informal listening, the reconstructions are judged to be "crisp" corresponding to good temporal resolution of transient and rapidly-moving speech events.